The modern multimodal transportation system provides an extensive network for human mobility and commodity exchange around the globe. As a consequence these interactions are often accompanied by disease and other biological infectious agents. This dissertation highlights the versatility of network models in quantifying the combined impact transportation systems, ecological systems and social networks have on the epidemiological process. A set of predictive models intended to compliment the current mathematical and simulation based modeling tools are introduced. The main contribution is the incorporation of dynamic infection data, which is becoming increasingly available, but is not accounted for in previous epidemiological models. Three main problems are identified. The objective of the first problem is to identify the path of infection (for a specific disease scenario) through a social contact network by invoking the use of network based optimization algorithms and individual infection reports. This problem parallels a novel and related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection. The second problem is a macroscopic application of the methodology introduced in the first problem. The new objective is to identify links in a transportation network responsible for spreading infection into new regions (spanning from a single source) using regional level infection data (e.g. when the disease arrived at a new location). The new network structure is defined by nodes which represent regions (cites, states, countries) and links representing travel routes. The third research problem is applicable to vector-borne diseases; those diseases which are transmitted to humans through the bite of an infected vector (i.e. mosquito), including dengue and malaria. The role of the vector in the infection process inherently alters the spreading process (compared to human contact diseases), which must be addressed in prediction models. The proposed objective is to quantify the risk posed by air travel in the global spread of these types of diseases. / text

This thesis has mainly focused on investigating the effect of communication network on the power system operation. The main objective of this research has been to develop a set of communication network simulation tools and verify their suitability for realistic cosimulation of a power system and an associated data-communication network within a power system simulation environment. Based on a background study, a set of communication components have been developed for the PSCAD/EMTDC power system simulation software, which can simulate communication delay and packet losses. Furthermore, an analytical method based on queuing theory has also been developed to evaluate the communication delay and packet loss probability of a typical PMU-PDC communication network. Finally, the communication components developed in this thesis have been integrated into the simulation of a wide area power system application to investigate the effect of communication network parameters on the power system operation.

This thesis has mainly focused on investigating the effect of communication network on the power system operation. The main objective of this research has been to develop a set of communication network simulation tools and verify their suitability for realistic cosimulation of a power system and an associated data-communication network within a power system simulation environment. Based on a background study, a set of communication components have been developed for the PSCAD/EMTDC power system simulation software, which can simulate communication delay and packet losses. Furthermore, an analytical method based on queuing theory has also been developed to evaluate the communication delay and packet loss probability of a typical PMU-PDC communication network. Finally, the communication components developed in this thesis have been integrated into the simulation of a wide area power system application to investigate the effect of communication network parameters on the power system operation.

This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks.

Reservoir simulation is an important tool for decision making and field development management. It enables reservoir engineers to predict reservoir production performance, update an existing model to reproduce monitoring data, assess alternative field development scenarios and design robust production optimization strategies by taking into account the existing uncertainties. A big obstacle in automating model calibration and production optimization approaches is the massive computation required to predict the response of real reservoirs under proposed changes in the model inputs. To speed up reservoir response predictions without compromising accuracy, fast surrogate models have been proposed. These models are either derived by preserving the physics of the involved processes (e.g. mass balance equations) to provide reliable long-range predictions or are developed based solely on statistical relations, in which case they can only provide short-range predictions due to the absence of the physical processes that govern the long-term behavior of the reservoir. We present an alternative solution that combines the advantages of both statistics-based and physics-based methods by deriving the flow predictions in complex two-dimensional models from one-dimensional flow network models. The existing injection/production wells in the original model form the nodes or vertices of the flow network. Each pair of wells (nodes) in the flow network is connected using a one-dimensional numerical simulation model; hence, the entire reservoir is reduced to a connected network of one-dimensional simulation models where the coupling between the individual one-dimensional models is enforced at the nodes where network edges intersect. The proposed flow network model provides a useful and fast tool for characterizing inter-well connectivity, estimating drainage volume between each pair of wells, and predicting reservoir production over an extended period of time for optimization purposes. We estimate the parameters of the flow network model using a robust training approach to ensure that the flow network model reproduces the response of the original full model under a wide range of development strategies. This step helps preserve the flow network model's predictive power during the production optimization when development strategies can change at different iterations. The robust networks training and the subsequent production optimization iterations are computationally efficient as they are performed with the faster flow network model. We demonstrate the effectiveness and applicability of our proposed flow network modeling approach to rapid production optimization using two-phase waterflooding simulations in synthetic and benchmark models.

Policy makers write policies that are implemented by actors at various levels of government. This results in policies that are implemented differently than how they were intended due to institutional contexts, pressure from the agency, personal beliefs, and collaboration between bureaucrats. This is especially true of natural resource policies, which are implemented at local scales by actors spread across the landscape. This research explores the effects that pressure from above, beliefs of individual actors, collaboration between actors, network structure, and distance between actors collectively have on policy implementation in federal natural resource agencies. A network modeling approach is employed to simulate the policy implementation process as a network of bureaucrats. Results indicate that network structure has little influence on the policy outcome, but adding distance alters the policy outcomes sensitivity to other parameters. The results illuminate the need to consider distance in policy implementation research. / 10000-01-01

The thesis deals with simulation of routing protocols. The aim is to create a functioning model of OSPF link-state protocol in the simulation framework OMNET++. OMNET++ is a discrete simulation environment which was created to provide means to build models of various network protocols and technologies. Chapters in the first part of the thesis focuson the theoretical foundation of OSPFv2 and OSPFv3 and their differences. Important data structures, finite state automata and communication techniques are described and the information is later used to implement the model itself. The chapters in the second part deal with the implementation of the model in C++. The created model reflects the functionality of OSPF on Cisco devices.

The arising environmental concerns make emission reduction from combustion devices one of the greatest challenges of the century. Modern dry low-NOx emission combustion systems often operate under lean premixed turbulent conditions. In order to design and operate these systems efficiently, it is necessary to have a thorough understanding of combustion process in these devices. In premixed combustion, flame speed determines the conversion rate of fuel. The flame speed under highly turbulent conditions is defined as turbulent flame speed. Turbulent flame speed depends on laminar flame speed, which is a property of the combustible mixture. The goal of this thesis is to estimate laminar flame speed and NOx emissions under certain conditions for specific industrial gas turbines. For this purpose, an in-house one-dimensional code, GENE-AC, is used. At first, a data validation is performed in order to select an optimized chemical reaction mechanism which can be used safely with the fuels of interest in gas turbines. Results show that GRI-Mech 3.0 performs well in most cases. This mechanism is selected for further simulations. Secondly, laminar flame speed is calculated using GRI-Mech 3.0 at SGT-800 conditions. Results show that at gas turbine conditions, increasing ambient temperature and fuel to air ratio enhances flame speed, mainly due to faster reaction rates. Moreover, laminar flame speed is highly affected by fuel composition. In particular, adding hydrogen to a fuel changes chemical processes significantly, because hydrogen is relatively light and highly diffusive. Calculations are conducted over a range of equivalence ratios and hydrogen fractions in methane at atmospheric as well as gas turbine operating conditions. Results reveal some trends for changes in laminar flame speed, depending on hydrogen content in the mixture. The final part of the thesis involves the development of a reactor network model for the SGT-700 combustor in order to predict NOx emissions. The network model is built in GENE-AC based on results from available computational fluid dynamics (CFD) simulations of the combustor. The model is developed for full load conditions with variable pilot fuel ratios. The NOx emissions are predicted using GRI-Mech 3.0 mechanism. A parametric study shows the dependency of NOx emissions on equivalence ratio and residence time. For SGT-700 running on natural gas, NOx emissions are fitted to measurement data by tuning equivalence ratio and residence time. The model is then tested for a range of ambient temperatures and fuel compositions. It is found that, although the model can correctly predict the trends of ambient temperature and fuel effects on NOx emissions, these effects are to some extent over-estimated. Using future engine tests and amending calibration can improve the results.

Multi-channel wireless networks represent a direction that future state-of-the-art fourth generation (4G) wireless communication standards evolve towards. The IEEE 802.16 family of standards, or referred to as WiMAX, has emerged as one of the most important 4G networks to provide high speed data communication in metropolitan areas. There will be huge challenges in designing the networking protocols to allow WiMAX to provide high quality of services. How to effectively control the errors in the wireless channels and how to efficiently manage the scarce spectrum and power resources in different communication scenarios are crucial for network performance. This thesis aims to solve these challenges to improve the performance of multi-channel wireless networks, using WiMAX as a representative, through a number of techniques. First, we take advantage of the favorable properties of network coding, and design the adaptive MAC-layer and symbol-level network coding protocols. They tightly integrate with WiMAX physical and MAC layers, effectively perform error control, and efficiently utilize scarce wireless spectrum. Second, we investigate multicast services and the femto-cell architecture in WiMAX, and offer a cooperative multicast scheduling protocol as well as a cognitive WiMAX architecture with femto cells. They implement dynamic resource allocation in the networks through techniques of cooperative communication and dynamic optimization. Evaluated with rigorous analysis and extensive simulations, our proposed protocols are able to achieve substantial performance improvement over traditional protocols in the literature.

Multi-channel wireless networks represent a direction that future state-of-the-art fourth generation (4G) wireless communication standards evolve towards. The IEEE 802.16 family of standards, or referred to as WiMAX, has emerged as one of the most important 4G networks to provide high speed data communication in metropolitan areas. There will be huge challenges in designing the networking protocols to allow WiMAX to provide high quality of services. How to effectively control the errors in the wireless channels and how to efficiently manage the scarce spectrum and power resources in different communication scenarios are crucial for network performance. This thesis aims to solve these challenges to improve the performance of multi-channel wireless networks, using WiMAX as a representative, through a number of techniques. First, we take advantage of the favorable properties of network coding, and design the adaptive MAC-layer and symbol-level network coding protocols. They tightly integrate with WiMAX physical and MAC layers, effectively perform error control, and efficiently utilize scarce wireless spectrum. Second, we investigate multicast services and the femto-cell architecture in WiMAX, and offer a cooperative multicast scheduling protocol as well as a cognitive WiMAX architecture with femto cells. They implement dynamic resource allocation in the networks through techniques of cooperative communication and dynamic optimization. Evaluated with rigorous analysis and extensive simulations, our proposed protocols are able to achieve substantial performance improvement over traditional protocols in the literature.